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        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data_5 when this report was generated.


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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.12

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2022-07-24, 10:23 based on data in: C:\Users\ASUS\Desktop\lfq_test_localize


        pmultiqc

        pmultiqc is a multiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms.

        Experimental Design

        This table shows the design of the experiment. I.e., which files and channels correspond to which sample/condition/fraction.

        You can see details about it in https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/classOpenMS_1_1ExperimentalDesign.html

        Showing 2/2 rows and 6/6 columns.
        Spectra FileFraction_GroupFractionLabelSampleMSstats_ConditionMSstats_BioReplicate
        SF_200217_pPeptideLibrary_pool1_HCDnlETcaD_OT_rep1.mzML111111
        SF_200217_pPeptideLibrary_pool1_HCDnlETcaD_OT_rep2.mzML211222

        HeatMap

        This heatmap shows a performance overview of the pipeline

        This plot shows the pipeline performance overview. Some metrics are calculated.

        • Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants; 1 = no contaminants
        • Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold, i.e. reaching 2^21 of 2^23 gives score 0.25.
        • Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?), in-source fragmentation, missed cleavages and buffer irregularities can cause a shift (see Bittremieux 2017, DOI: 10.1002/mas.21544 ).
        • Heatmap score [MC]: the fraction (0% - 100%) of fully cleaved peptides per Raw file
        • Heatmap score [MC Var]: each Raw file is scored for its deviation from the ‘average’ digestion state of the current study.
        • Heatmap score [ID rate over RT]: Judge column occupancy over retention time. Ideally, the LC gradient is chosen such that the number of identifications (here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles. Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient optimization.Scored using ‘Uniform’ scoring function. i.e. constant receives good score, extreme shapes are bad.
        • Heatmap score [MS2 Oversampling]: The percentage of non-oversampled 3D-peaks. An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file. For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides.
        • Heatmap score [Pep Missing]: Linear scale of the fraction of missing peptides.
        loading..

        Summary Table

        This table shows the quantms pipeline summary statistics

        This table shows the quantms pipeline summary statistics

        Showing 1/1 rows and 5/5 columns.
        #MS2 Spectra#Identified MS2 Spectra%Identified MS2 Spectra#Peptides Identified#Proteins Identified#Proteins Quantified
        5057
        428
        8.46%
        42
        39
        35

        Pipeline Result Statistics

        This plot shows the quantms pipeline final result

        This plot shows the quantms pipeline final result. Including Sample Name、Possible Study Variables、identified the number of peptide in the pipeline、 and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained from the out_msstats file. You can also remove the decoy with the remove_decoy parameter.

        Showing 2/2 rows and 7/7 columns.
        Spectra FileSample NameConditionFraction#Peptide IDs#Unambiguous Peptide IDs#Modified Peptide IDs#Protein (group) IDs
        SF_200217_pPeptideLibrary_pool1_HCDnlETcaD_OT_rep1.mzML
        1
        1
        1
        122
        58
        96
        38
        SF_200217_pPeptideLibrary_pool1_HCDnlETcaD_OT_rep2.mzML
        2
        2
        1
        128
        59
        102
        38

        Number of Peptides Per Protein

        This plot shows the number of peptides per proteins in quantms pipeline final result

        This statistic is extracted from the out_msstats file. Proteins supported by more peptide identifications can constitute more confident results.

        loading..

        Spectra Tracking

        This plot shows the tracking of the number of spectra along the quantms pipeline

        This table shows the changes in the number of spectra corresponding to each input file during the pipeline operation. And the number of peptides finally identified and quantified is obtained from the PSM table in the mzTab file. You can also remove decoys with the remove_decoy parameter.:

        • MS1_Num: The number of MS1 spectra extracted from mzMLs
        • MS2_Num: The number of MS2 spectra extracted from mzMLs
        • MSGF: The Number of spectra identified by MSGF search engine
        • Comet: The Number of spectra identified by Comet search engine
        • PSMs from quant. peptides: extracted from PSM table in mzTab file

        • Peptides quantified: extracted from PSM table in mzTab file

        Showing 2/2 rows and 6/6 columns.
        Spectra File#MS1 Spectra#MS2 SpectraMSGFComet#PSMs from quant. peptides#Peptides quantified
        SF_200217_pPeptideLibrary_pool1_HCDnlETcaD_OT_rep1.mzML
        4172
        2592
        1347
        1269
        214
        40
        SF_200217_pPeptideLibrary_pool1_HCDnlETcaD_OT_rep2.mzML
        4246
        2465
        1273
        1242
        214
        41

        Distribution of precursor charges

        This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.

        This information can be used to identify potential ionization problems including many 1+ charges from an ESI ionization source or an unexpected distribution of charges. MALDI experiments are expected to contain almost exclusively 1+ charged ions. An unexpected charge distribution may furthermore be caused by specific search engine parameter settings such as limiting the search to specific ion charges.

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        Number of Peaks per MS/MS spectrum

        This chart represents a histogram containing the number of peaks per MS/MS spectrum in a given experiment. This chart assumes centroid data. Too few peaks can identify poor fragmentation or a detector fault, as opposed to a large number of peaks representing very noisy spectra. This chart is extensively dependent on the pre-processing steps performed to the spectra (centroiding, deconvolution, peak picking approach, etc).

        loading..

        Peak Intensity Distribution

        This is a histogram representing the ion intensity vs. the frequency for all MS2 spectra in a whole given experiment. It is possible to filter the information for all, identified and unidentified spectra. This plot can give a general estimation of the noise level of the spectra.

        Generally, one should expect to have a high number of low intensity noise peaks with a low number of high intensity signal peaks. A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or potential experimental problems. In the case of data reuse this plot can be useful in identifying the requirement for pre-processing of the spectra prior to any downstream analysis. The quality of the identifications is not linked to this data as most search engines perform internal spectrum pre-processing before matching the spectra. Thus, the spectra reported are not necessarily pre-processed since the search engine may have applied the pre-processing step internally. This pre-processing is not necessarily reported in the experimental metadata.

        loading..

        Oversampling Distribution

        An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file.

        For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. Oversampling occurs in low-complexity samples or long LC gradients, as well as undersized dynamic exclusion windows for data independent acquisitions.

                    * Heatmap score [EVD: MS2 Oversampling]: The percentage of non-oversampled 3D-peaks.
        
        loading..

        Delta Mass

        This chart represents the distribution of the relative frequency of experimental precursor ion mass (m/z) - theoretical precursor ion mass (m/z).

        Mass deltas close to zero reflect more accurate identifications and also that the reporting of the amino acid modifications and charges have been done accurately. This plot can highlight systematic bias if not centered on zero. Other distributions can reflect modifications not being reported properly. Also it is easy to see the different between the target and the decoys identifications.

        loading..

        Summary of Search Engine

        These plots contain search scores and PEPs counts for different search engines in different files, and they also contain a summary of the consensus PSMs if two search engines are used

        This statistic is extracted from idXML files.


        Summary of Search Scores

                * SpecEvalue : Spectral E-values, the search score of MSGF
                * xcorr : cross-correlation scores, the search score of Comet
        
        loading..
        loading..

        Summary of Posterior Error Probabilities

                * PEP : Posterior Error Probability
        
        loading..

        Summary of consensus PSMs

        loading..

        Peptides Quantification Table

        This plot shows the quantification information of peptides in the final result (mainly the mzTab file).

        The quantification information of peptides is obtained from the MSstats input file. The table shows the quantitative level and distribution of peptides in different study variables, run and peptiforms. The distribution show all the intensity values in a bar plot above and below the average intensity for all the fractions, runs and peptiforms.

        • BestSearchScore: It is equal to 1 - min(Q.Value) for DIA datasets. Then it is equal to 1 - min(best_search_engine_score[1]), which is from best_search_engine_score[1] column in mzTab peptide table for DDA datasets.
        • Average Intensity: Average intensity of each peptide sequence across all conditions with NA=0 or NA ignored.
        • Peptide intensity in each condition (Eg. CT=Mixture;CN=UPS1;QY=0.1fmol): Summarize intensity of fractions, and then mean intensity in technical replicates/biological replicates separately. Click Show replicates to switch to bar plots for every replicate.
        Showing 50/50 rows and 8/8 columns.
        PeptideIDPeptideSequenceProteinNameBestSearchScoreAverage Intensity1212
        1
        .(Acetyl)ESKS(Phospho)SPRPTAEK
        IPI00004344_2
        0.53492
        5.610
        5.60981
        0.00000
        2
        .(Acetyl)FGES(Phospho)DTENQNNK
        IPI00328149_1;IPI00328149_2
        0.96146
        6.504
        0.00000
        6.50443
        3
        .(Acetyl)GQEY(Phospho)LILEK
        IPI00000878_1
        0.98825
        5.976
        5.99197
        5.95929
        4
        .(Acetyl)IKS(Phospho)YSFPK
        IPI00018597_2
        0.65975
        6.350
        0.00000
        6.35000
        5
        .(Acetyl)IKSYS(Phospho)FPK
        IPI00018597_2
        0.76985
        6.412
        0.00000
        6.41175
        6
        .(Acetyl)NFSAAKS(Phospho)LLNK
        IPI00216378_1
        0.99205
        6.550
        6.54989
        0.00000
        7
        .(Acetyl)SSS(Phospho)FREM(Oxidation)DGQPER
        IPI00216969_1;IPI00216969_11;IPI00216969_4
        0.70391
        5.081
        0.00000
        5.08118
        8
        .(Acetyl)SSS(Phospho)FREM(Oxidation)ENQPHK
        IPI00329488_10;IPI00329488_16;IPI00329488_3
        0.70447
        6.395
        0.00000
        6.39527
        9
        .(Acetyl)ST(Phospho)LVLHDLLK
        IPI00004497_3;IPI00004497_5
        0.76416
        6.316
        6.31638
        0.00000
        10
        .(Acetyl)VGSLT(Phospho)PPSSPK
        IPI00298977_3
        0.99376
        6.618
        6.11335
        6.84511
        11
        .(Acetyl)VGSLTPPS(Phospho)SPK
        IPI00298977_3
        0.91871
        6.175
        6.11335
        6.22831
        12
        .(Acetyl)VPAS(Phospho)PLPGLER
        IPI00555838_4
        0.98685
        5.699
        5.59887
        5.78053
        13
        .(Acetyl)YIEDEDY(Phospho)YK
        IPI00029702_1;IPI00029702_2;IPI00029702_3
        0.93931
        5.705
        5.67148
        5.73636
        14
        ADENYYK
        IPI00018597_1;IPI00018597_3
        0.97628
        8.061
        7.91447
        8.17016
        15
        AGGKPS(Phospho)Q(Deamidated)SPSQEAAGEAVLGAK
        IPI00216969_13;IPI00216969_5
        0.99981
        6.783
        6.47590
        6.96131
        16
        AGGKPS(Phospho)QSPSQEAAGEAVLGAK
        IPI00216969_13;IPI00216969_5
        1.00000
        8.490
        0.00000
        8.48982
        17
        AGGKPSQSPSQEAAGEAVLGAK
        IPI00216969_13;IPI00216969_5
        1.00000
        7.496
        7.56839
        7.40949
        18
        AVGM(Oxidation)PSPVS(Phospho)PK
        IPI00004344_1
        0.99989
        8.978
        8.90400
        9.04089
        19
        AVGM(Oxidation)PSPVSPK
        IPI00004344_1
        0.99969
        8.006
        7.98494
        8.02702
        20
        DKS(Phospho)PSSLLEDAK
        IPI00329488_12
        0.99920
        8.404
        8.53403
        7.88253
        21
        DKSPSSLLEDAK
        IPI00329488_12
        0.99905
        7.156
        7.13872
        7.18808
        22
        ES(Phospho)KSSPRPTAEK
        IPI00004344_2
        0.68741
        6.295
        0.00000
        6.29454
        23
        ESKS(Phospho)SPRPTAEK
        IPI00004344_2
        0.99709
        6.396
        6.40859
        6.38334
        24
        ESKSSPRPT(Phospho)AEK
        IPI00004344_2
        0.99898
        6.384
        6.29592
        6.45703
        25
        ESKSSPRPTAEK
        IPI00004344_2
        0.93728
        5.591
        5.59084
        0.00000
        26
        FGES(Phospho)DTENQNNK
        IPI00328149_1;IPI00328149_2
        0.99993
        6.489
        6.07688
        6.69629
        27
        FGESDT(Phospho)ENQNNK
        IPI00328149_1;IPI00328149_2
        0.69551
        6.696
        0.00000
        6.69629
        28
        FGESDTENQNNK
        IPI00328149_1;IPI00328149_2
        0.99994
        8.232
        8.11194
        8.32535
        29
        GGFFSS(Phospho)FM(Oxidation)K
        IPI00329488_13
        0.99360
        8.116
        8.05856
        8.16654
        30
        GGFFSS(Phospho)FMK
        IPI00329488_13
        0.99453
        7.353
        7.43754
        7.24814
        31
        GGFFSSFM(Oxidation)K
        IPI00329488_13
        0.99644
        7.202
        7.39817
        6.83604
        32
        GHLS(Phospho)EGLVTK
        IPI00003431
        0.99762
        8.979
        8.92574
        9.02600
        33
        GHLSEGLVT(Phospho)K
        IPI00003431
        0.99037
        9.227
        9.22653
        0.00000
        34
        GHLSEGLVTK
        IPI00003431
        0.99836
        7.600
        6.45096
        7.88497
        35
        GQEY(Phospho)LILEK
        IPI00000878_1
        0.97736
        9.330
        9.27492
        9.37855
        36
        GQEYLILEK
        IPI00000878_1
        0.98775
        8.784
        8.69584
        8.85666
        37
        IGEGT(Phospho)Y(Phospho)GVVYK
        IPI00026689_1
        0.99900
        8.095
        8.03307
        8.14958
        38
        IGEGTYGVVYK
        IPI00026689_1
        0.55501
        6.190
        6.19035
        0.00000
        39
        IKS(Phospho)Y(Phospho)SFPK
        IPI00018597_2
        0.96943
        6.649
        6.64854
        0.00000
        40
        IKS(Phospho)YS(Phospho)FPK
        IPI00018597_2
        0.63304
        6.882
        0.00000
        6.88206
        41
        IKSY(Phospho)SFPK
        IPI00018597_2
        0.99465
        8.972
        8.90248
        9.03261
        42
        IKSYS(Phospho)FPK
        IPI00018597_2
        0.91522
        6.689
        5.60141
        6.97227
        43
        IKSYSFPK
        IPI00018597_2
        0.99490
        7.784
        7.80009
        7.76760
        44
        KTS(Phospho)PLNFK
        IPI00029132_1
        0.99498
        8.785
        0.00000
        8.78506
        45
        KTSPLNFK
        IPI00029132_1
        0.99820
        7.568
        7.49833
        7.62754
        46
        LM(Oxidation)TGDT(Phospho)YTAHAGAK
        IPI00329488_1;IPI00329488_14;IPI00329488_4
        1.00000
        7.688
        7.56309
        7.78453
        47
        LM(Oxidation)TGDTY(Phospho)TAHAGAK
        IPI00329488_1;IPI00329488_14;IPI00329488_4
        0.99957
        7.663
        7.57509
        7.73651
        48
        LMTGDT(Phospho)YTAHAGAK
        IPI00329488_1;IPI00329488_14;IPI00329488_4
        1.00000
        7.108
        6.83835
        7.27315
        49
        LQT(Phospho)VHSIPLTINK
        IPI00004497_1;IPI00004497_10
        0.99990
        8.623
        8.61177
        8.64604
        50
        LQTVHS(Phospho)IPLTINK
        IPI00004497_1;IPI00004497_10
        0.99022
        8.590
        8.52573
        8.64604
        First Page Previous PageNext Page Last PagePage/Total Pages

        Protein Quantification Table

        This plot shows the quantification information of proteins in the final result (mainly the mzTab file).

        The quantification information of proteins is obtained from the msstats input file. The table shows the quantitative level and distribution of proteins in different study variables and run.

        • Peptides_Number: The number of peptides for each protein.
        • Average Intensity: Average intensity of each protein across all conditions with NA=0 or NA ignored.
        • Protein intensity in each condition (Eg. CT=Mixture;CN=UPS1;QY=0.1fmol): Summarize intensity of peptides.

        Click Show replicates to switch to bar plots of quantities in each replicate.

        Showing 35/35 rows and 7/7 columns.
        ProteinIDProteinNamePeptides_NumberAverage Intensity1212
        1
        IPI00000878_1
        3
        24
        23.96273
        24.19450
        2
        IPI00003431
        3
        26
        24.60323
        16.91096
        3
        IPI00004344_1
        2
        17
        16.88894
        17.06791
        4
        IPI00004344_2
        5
        30
        23.90517
        19.13491
        5
        IPI00004497_1;IPI00004497_10
        3
        24
        23.57992
        23.69570
        6
        IPI00004497_3;IPI00004497_5
        2
        15
        15.05181
        8.21027
        7
        IPI00014266
        2
        17
        16.80433
        16.88781
        8
        IPI00015287_1;IPI00015287_2;IPI00015287_3
        4
        30
        23.76707
        29.81118
        9
        IPI00016932_1;IPI00016932_3;IPI00016932_4
        1
        6
        6.26872
        0.00000
        10
        IPI00018597_1;IPI00018597_3
        1
        8
        7.91447
        8.17016
        11
        IPI00018597_2
        9
        63
        42.20377
        56.90029
        12
        IPI00026689_1
        2
        14
        14.22342
        8.14958
        13
        IPI00029132_1
        2
        16
        7.49833
        16.41260
        14
        IPI00029702_1;IPI00029702_2;IPI00029702_3
        4
        30
        30.07867
        21.36763
        15
        IPI00145805
        2
        17
        17.00593
        17.11558
        16
        IPI00216378_1
        5
        34
        21.58144
        27.69589
        17
        IPI00216378_2
        2
        13
        12.54625
        6.29161
        18
        IPI00216969_13;IPI00216969_5
        3
        23
        14.04429
        22.86062
        19
        IPI00216969_1;IPI00216969_11;IPI00216969_4
        8
        61
        40.80865
        61.10862
        20
        IPI00216969_2
        4
        29
        22.35039
        22.68735
        21
        IPI00216969_3;IPI00329488_8
        2
        16
        13.81493
        16.10652
        22
        IPI00298977_3
        5
        33
        24.56845
        33.36440
        23
        IPI00298977_5;IPI00298978
        3
        24
        23.93853
        24.29957
        24
        IPI00301263
        3
        23
        13.35663
        23.50535
        25
        IPI00328149_1;IPI00328149_2
        4
        28
        14.18881
        28.22236
        26
        IPI00329488_10;IPI00329488_16;IPI00329488_3
        7
        50
        43.27897
        40.24410
        27
        IPI00329488_11;IPI00329491
        7
        55
        33.27350
        55.95427
        28
        IPI00329488_12
        5
        38
        30.45287
        37.33706
        29
        IPI00329488_13
        3
        23
        22.89427
        22.25072
        30
        IPI00329488_15;IPI00329488_2;IPI00329488_5;IPI00329488_7;IPI00329488_9
        4
        29
        22.30267
        29.73705
        31
        IPI00329488_1;IPI00329488_14;IPI00329488_4
        3
        22
        21.97653
        22.79420
        32
        IPI00329638_1;IPI00329638_3;IPI00329638_4;IPI00329638_5
        2
        17
        16.64866
        16.61169
        33
        IPI00413961_1
        4
        30
        30.24704
        30.17541
        34
        IPI00555838_2;IPI00555838_5
        2
        16
        15.48110
        16.20808
        35
        IPI00555838_4
        3
        24
        23.44378
        23.59718
        First Page Previous PageNext Page Last PagePage/Total Pages